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Record W1989855304 · doi:10.1097/jom.0b013e3182307845

Soft-Tissue Sarcoma and Pesticides Exposure in Men

2011· article· en· W1989855304 on OpenAlexaffabout
Punam Pahwa, Chandima Karunanayake, James A. Dosman, John J. Spinelli

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversity of SaskatchewanOntario Institute for Cancer ResearchRoyal University HospitalCancer Care OntarioUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsDiazinonLogistic regressionMedicineIncidence (geometry)Environmental healthPesticidePopulationToxicologyDemographyInternal medicineBiologyMathematics

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective was to investigate the putative associations of specific pesticides with soft-tissue sarcoma (STS). METHODS: A Canadian population-based case-control study conducted in six provinces was used in this analysis. The study design consisted of two stages: a self-administered postal questionnaire and a telephone interview for those reporting pesticides exposure of 10 hours per year or more; and a 15% random sample of the remainder. Conditional logistic regression was used to fit the statistical models. RESULTS: A positive history of cancer among first-degree relatives and exposure to aldrin and diazinon were statistically significant independent predictors of an increased risk for STS, whereas diagnosis of whopping cough lowered the risk of STS. CONCLUSIONS: The incidence of STS was associated with specific insecticides after adjustment for other independent predictors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.280
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2011
Admission routes2
Has abstractyes

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